SOTAVerified

Graph Representation Learning

The goal of Graph Representation Learning is to construct a set of features (‘embeddings’) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole.

Source: SIGN: Scalable Inception Graph Neural Networks

Papers

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Showing 1–50 of 982 papers

TitleStatusHype
ROLAND: Graph Learning Framework for Dynamic GraphsCode3
A Survey of Pretraining on Graphs: Taxonomy, Methods, and ApplicationsCode2
Graph Domain Adaptation: Challenges, Progress and ProspectsCode2
NeuralKG-ind: A Python Library for Inductive Knowledge Graph Representation LearningCode2
Explanation-Preserving Augmentation for Semi-Supervised Graph Representation LearningCode2
VideoSAGE: Video Summarization with Graph Representation LearningCode2
Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product NetworksCode2
Towards Relation-centered Pooling and Convolution for Heterogeneous Graph Learning NetworksCode2
CogDL: A Comprehensive Library for Graph Deep LearningCode2
Recipe for a General, Powerful, Scalable Graph TransformerCode2
Dynamic Graph Representation with Knowledge-aware Attention for Histopathology Whole Slide Image AnalysisCode2
Do Transformers Really Perform Bad for Graph Representation?Code2
Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation LearningCode2
Graph Neural Networks for Natural Language Processing: A SurveyCode2
Effect of Choosing Loss Function when Using T-batching for Representation Learning on Dynamic NetworksCode2
Graph4Rec: A Universal Toolkit with Graph Neural Networks for Recommender SystemsCode2
LambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph EmbeddingsCode2
Structure-Aware Transformer for Graph Representation LearningCode2
A Survey on Knowledge Graphs: Representation, Acquisition and ApplicationsCode2
Does Graph Distillation See Like Vision Dataset Counterpart?Code1
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation LearningCode1
Does Invariant Graph Learning via Environment Augmentation Learn Invariance?Code1
Deep Graph Mapper: Seeing Graphs through the Neural LensCode1
DiffKG: Knowledge Graph Diffusion Model for RecommendationCode1
Audio Event-Relational Graph Representation Learning for Acoustic Scene ClassificationCode1
Deep Graph Representation Learning and Optimization for Influence MaximizationCode1
Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily DiscriminatingCode1
Disentangle-based Continual Graph Representation LearningCode1
AutoGCL: Automated Graph Contrastive Learning via Learnable View GeneratorsCode1
Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power SystemCode1
Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training TasksCode1
Deep Graph Contrastive Representation LearningCode1
Multi-hop Attention Graph Neural NetworkCode1
Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across CitiesCode1
Certifiably Robust Graph Contrastive LearningCode1
A Graph is Worth K Words: Euclideanizing Graph using Pure TransformerCode1
Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand PredictionCode1
A step towards neural genome assemblyCode1
Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph ClassificationCode1
A Generalization of ViT/MLP-Mixer to GraphsCode1
A Fair Comparison of Graph Neural Networks for Graph ClassificationCode1
A Gentle Introduction to Deep Learning for GraphsCode1
A Structure-Aware Framework for Learning Device Placements on Computation GraphsCode1
CCGL: Contrastive Cascade Graph LearningCode1
COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive LearningCode1
Class-Imbalanced Learning on Graphs: A SurveyCode1
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt TuningCode1
A Large-Scale Database for Graph Representation LearningCode1
Algorithm and System Co-design for Efficient Subgraph-based Graph Representation LearningCode1
A critical look at the evaluation of GNNs under heterophily: Are we really making progress?Code1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Pi-net-linearError (mm)0.47—Unverified